發(fā)布時(shí)間：2018-10-09 18:34

【摘要】：基于視頻圖像煙霧檢測(cè)技術(shù)相對(duì)基于傳感器原理的煙霧探測(cè)技術(shù)具有受環(huán)境影響小、響應(yīng)速度快以及檢測(cè)結(jié)果直觀等優(yōu)勢(shì),是實(shí)現(xiàn)火災(zāi)早期預(yù)警的重要手段�；馂�(zāi)發(fā)生初期,通常伴隨著煙霧的產(chǎn)生,本文的主要研究工作是通過(guò)監(jiān)控場(chǎng)景來(lái)判斷是否有煙霧產(chǎn)生。現(xiàn)有的煙霧識(shí)別方法均是從圖像中直接提取煙霧的可視化特征,所提取的特征包含背景和煙霧兩部分信息,致使無(wú)法有效描述煙霧的特征,從而影響了煙霧識(shí)別的精度。針對(duì)該問(wèn)題,本文從成像原理的角度出發(fā),認(rèn)為一幅圖像是由背景圖像與煙霧圖像線(xiàn)性混合而成的,提出煙霧線(xiàn)性表達(dá)模型及其最優(yōu)化問(wèn)題,并提出成分分離算法求解該問(wèn)題。成分分離算法,即通過(guò)從當(dāng)前圖像中單獨(dú)分離出煙霧成分后,提取其紋理特征進(jìn)而實(shí)現(xiàn)煙霧識(shí)別。該算法以矩形塊為計(jì)算單位,根據(jù)相鄰像素間像素具有相似性,構(gòu)建局部平滑模型;同時(shí),從整個(gè)紋理結(jié)構(gòu)的角度看,純煙霧圖像位于一個(gè)低維的子空間,并且可以采用主成分分析來(lái)確定純煙霧圖像的子空間進(jìn)而描述純煙霧圖像,因此,構(gòu)建主成分模型。通過(guò)采用這兩個(gè)模型對(duì)圖像進(jìn)行成分分離,得到純煙霧成分,然后利用LBP算子提取其紋理特征,最后代入支持向量機(jī)分類(lèi)器中判斷煙霧是否存在,從而實(shí)現(xiàn)煙霧識(shí)別。通過(guò)合成圖像以及真實(shí)視頻數(shù)據(jù)對(duì)本文提出的算法性能進(jìn)行評(píng)估,從檢測(cè)精度方面與Toreyin和Tian煙霧識(shí)別算法進(jìn)行對(duì)比分析。實(shí)驗(yàn)結(jié)果表明,該算法在室內(nèi)、室外、背景復(fù)雜的情況下均能有效識(shí)別出煙霧,正檢率均在93%以上,誤檢率以及漏檢率均在在4%以下;同時(shí),針對(duì)全覆蓋濃煙、全覆蓋薄煙、局部覆蓋度大于50%的煙、局部覆蓋度小于50%的煙四類(lèi)不同類(lèi)型的煙霧圖像,本文算法平均精度高于Toreyin和Tian煙霧識(shí)別算法,平均檢測(cè)精度為91.3%,誤檢率為5.4%,漏檢率為3.3%。
[Abstract]:Compared with the smoke detection technology based on sensor principle, the video image smoke detection technology has the advantages of less environmental impact, fast response and intuitive detection results. It is an important means to achieve early warning of fire. In the early stage of fire, smoke is usually accompanied by smoke. The main work of this paper is to judge whether smoke is produced by monitoring scene. The existing methods of smoke recognition are to extract the visual features of smoke directly from the image. The extracted features include background and smoke information, which can not effectively describe the characteristics of smoke, thus affecting the accuracy of smoke recognition. In this paper, from the angle of imaging principle, we think that an image is a linear mixture of background image and smoke image, propose a smoke linear representation model and its optimization problem, and propose a component separation algorithm to solve the problem. The component separation algorithm is to extract the texture feature of the smoke component from the current image and then to realize the smoke recognition by separating the smoke component separately from the current image. The algorithm takes the rectangular block as the unit of calculation and constructs a local smoothing model according to the pixel similarity between adjacent pixels. At the same time, from the point of view of the whole texture structure, the pure smog image is located in a low-dimensional subspace. And the principal component analysis can be used to determine the subspace of the pure smoke image and then describe the pure smoke image. Therefore, the principal component model is constructed. By using these two models to separate the components of the image, the pure smoke components are obtained, and then the texture features are extracted by using the LBP operator. Finally, the smoke is judged in the support vector machine classifier to realize smoke recognition. The performance of the proposed algorithm is evaluated by synthetic images and real video data, and compared with Toreyin and Tian smoke recognition algorithms in terms of detection accuracy. The experimental results show that the algorithm can effectively identify smoke in indoor, outdoor and complex background, the positive detection rate is over 93%, the false detection rate and missed detection rate are below 4%. The average accuracy of this algorithm is higher than that of Toreyin and Tian smoke recognition algorithms. The average detection accuracy is 91.3%, the false detection rate is 5.4%, and the missed detection rate is 3.3%.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

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